Practical Deep Reinforcement Learning Approach for Stock Trading

TL;DR

This paper presents a deep reinforcement learning method for stock trading that adapts to market dynamics and outperforms traditional strategies in return and risk-adjusted metrics.

Contribution

It introduces a novel deep reinforcement learning approach specifically designed for stock trading, demonstrating superior performance over traditional methods.

Findings

Outperforms Dow Jones Industrial Average in returns

Achieves higher Sharpe ratio than baseline strategies

Demonstrates adaptability to market changes

Abstract

Stock trading strategy plays a crucial role in investment companies. However, it is challenging to obtain optimal strategy in the complex and dynamic stock market. We explore the potential of deep reinforcement learning to optimize stock trading strategy and thus maximize investment return. 30 stocks are selected as our trading stocks and their daily prices are used as the training and trading market environment. We train a deep reinforcement learning agent and obtain an adaptive trading strategy. The agent's performance is evaluated and compared with Dow Jones Industrial Average and the traditional min-variance portfolio allocation strategy. The proposed deep reinforcement learning approach is shown to outperform the two baselines in terms of both the Sharpe ratio and cumulative returns.